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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: Jun 8, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Computational acceleration for MR image reconstruction in partially parallel imaging.

Xiaojing Ye¹, Yunmei Chen, Feng Huang

¹Department of Mathematics, University of Florida, Gainesville, FL 32611, USA. xye@ufl.edu

IEEE Transactions on Medical Imaging

|September 14, 2010

Summary

This summary is machine-generated.

This study introduces a fast numerical algorithm for total variation and l(1) (TVL1) image reconstruction, significantly reducing computational cost and iterations for faster, high-quality results in parallel MRI.

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Area of Science:

Medical Imaging
Computational Science
Image Reconstruction

Background:

Partially parallel magnetic resonance imaging (MRI) requires efficient image reconstruction techniques.
Existing methods for total variation and l(1) (TVL1) based reconstruction can be computationally intensive.

Purpose of the Study:

To develop a fast numerical algorithm for TVL1-based image reconstruction.
To improve computational efficiency and convergence speed for MRI applications.

Main Methods:

Variable splitting method to reduce computational cost.
Barzilai-Borwein step size selection for faster convergence.
Application to partially parallel magnetic resonance imaging data.

Main Results:

The proposed algorithm significantly reduces the number of iterations required.
Achieves comparable or superior image quality with less computational cost.
Outperforms existing operator splitting and Bregman operator splitting methods.

Conclusions:

The novel algorithm offers a computationally efficient solution for TVL1-based image reconstruction.
Demonstrates practical advantages for clinical partially parallel MRI.
Enables faster and potentially more accessible MRI procedures.